{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开源数据集\n",
    "# 自己标注"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 滑动窗口\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征处理\n",
    "# 已提取，可区分性，不变性\n",
    "# LBP HOG HAAR\n",
    "# Haar-like\n",
    "# 1.彩图转灰度图，白色矩形像素和-黑色矩形像素和，类似一个卷积操作？左上-右下；1x1,2x2,4x4,3x4\n",
    "# 2.化简运算，A-B-C=D，积分图运算方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 支持向量机\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获得弱分类器\n",
    "# 1.对于所有样本图片，计算其中一种特征的haar特征\n",
    "# 2.统计正负样本的haar特征均值\n",
    "# 3.找到介于正负样本均值之间的一个阈值，使用此阈值来区分正负样本准确率最高\n",
    "# 4.对所有haar特征都找到这个最佳阈值，所有特征投票，提高分类效果\n",
    "\n",
    "# 提高被误判样本的权重\n",
    "# 错误率 ε= Swrong/Sall \n",
    "# 分类器权重 α=0.5ln(1-ε)/ε\n",
    "# 样本权重更新 Wt+1,i = Wt,i*eε/sum(Wt)\n",
    "# 更新样本权重，使用带有权重的样本训练第二个分类器\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为弱分类器分配权重\n",
    "# 错误率 ε= Swrong/Sall \n",
    "# 分类器权重 α=0.5ln(1-ε)/ε\n",
    "# 加权求和\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
